Learn Dart, Microsoft and Reactfire

Just about every tool in our accessibility auditing toolkit can help us identify any issues with sufficient color contrast ratio on a web page.  The react-axe library and axe browser extension both report findings on all elements with insufficient contrast ratios.  The tota11y browser extension will annotate any elements with their contrast ratios and color those that are insufficient in red.  

Additionally, we need to use our experience tools to see what some of our users out there may we seeing.  We need to test with high contrast browser extensions as well as the display settings in Windows and Mac.  If our users with visual impairments are using these tools we need to see what our application looks like using them because we may find that under a certain mode some things may suddenly be harder to read or virtually invisible.

## Resources
* https://www.scottohara.me/blog/2019/02/12/high-contrast-aria-and-images.html

Test for Insufficient Color Contrast Accessibility Issues

We should not use color alone as the only visual means of conveying information, indicating an action, prompting a response, or distinguishing a visual element.  Users may not be able to see/understand the differences in color and require additional indicators.

Along with color, use additional indicators such as:
* typography
* shapes
* grids
* spaces
* and/or allocate more weight to important elements

## References:
* http://www.particletree.com/features/interfaces-and-color-blindness/
* https://www.smashingmagazine.com/2014/10/color-contrast-tips-and-tools-for-accessibility/

Use More than Color Alone to Convey Information in a Web Page

Flutter allows you to build beautiful native apps on iOS and Android from a single codebase.

flutter

Extension methods allow you to add new methods to classes without directly changing its original interface or creating a derived class. We will explore examples of working with this feature by extending some inbuilt types.

Learn more about Extension Methods at https://medium.com/dartlang/extension-methods-2d466cd8b308

Add New Methods to inbuilt Classes and Interfaces with Extension Methods in Dart

We will learn how to work with Lists using a variety of methods made available in the **dart:core** library. We will explore the top methods for working with List type collections.

Learn more about Lists at https://api.dartlang.org/stable/2.2.0/dart-core/List-class.html

Manipulate Lists/Arrays in Dart

We will learn how to work with Strings using a variety of methods made available in the **dart:core** library. We will explore the top methods for working with String values.

Learn more about Strings at https://api.dartlang.org/stable/2.2.0/dart-core/String-class.html

Manipulate Strings in Dart

In this lesson, we will cover Interfaces and Mixins. Interfaces act as a contract containing properties and methods that a class must define once it “implements” this interface. Mixins are Dart’s way of sharing properties and methods from multiple classes, since by design Dart adopts a single-inheritance model.

Define Interfaces and Share Class Members through Mixins in Dart

Streams represent a sequence of asynchronous events. Each event is either a data event, also called an element of the stream, or an error event, which is a notification that something has failed. When a stream has emitted all its events, a single "done" event will notify the listener that the end has been reached. In this lesson, we will learn how we can capture and handle streaming data, working with various Stream classes.

Capture and Handle Data Sequences with Streams in Dart

Pub is a package management tool that makes it easy for you to install, use, share Dart libraries, command-line tools and other assets. It comes as part of the Dart SDK, and requires a dependency file called pubspec.yaml.

Manage Package Dependencies with Pub

Futures represent a computation that does not complete immediately. Where a normal function returns a result, an asynchronous function returns a Future that will eventually contain the result. These are similar to Promises in JavaScript. In this lesson, we will learn how to program asynchronously by writing logic to capture values that are returned at a later time.

Capture and Return Asynchronous Values with Futures in Dart

Dart is an object-oriented language you can use for building web applications, server-side code, and even mobile apps. It’s expressive, powerful, and has a friendly learning curve. 

In this course, we will look at the syntax and semantics of the Dart programming language. We will also cover various aspects of the Dart ecosystem, including its tools and packages that help make you a productive developer. This knowledge is applicable when developing software for client, server, and especially mobile with the Flutter framework. 

This course is beginner-friendly, but is well-suited for experience programmers as well. 

Dart is an open-source, scalable programming language, with robust libraries and runtimes, for building web, server, and mobile apps.

dart

Get Started with Dart

In this lesson we’ll learn how to fetch & then display data from an API over the network in a Flutter application using the http package.

Fetching & Displaying Data from an API in Flutter using the http Package

In this lesson, you will write a basic HTTP server in the Dart programming language. We will be using the HttpServer class from Dart's inbuilt 'dart:io' library to achieve this.

Dart is an object-oriented programming language by Google, which aims to help the developer build modern web applications. It covers client, server and now mobile with Flutter. It comes with a range of tools including a virtual machine, core libraries, and package management repository, lending enough ammunition to start your next project.

Learn more at https://dartlang.org


Write a Basic HTTP Server in Dart

Express is a minimal and flexible Node.js web application framework that provides a robust set of features for web and mobile applications.

express

To replicate your production application its helpful when developing a local NodeJS Express application is running under SSL. 

There are some web technologies which do not play nicely with standard http and its likely to become more common e.g. Web Push Notifications.

This lesson will show you how to create a local SSL certificate for localhost and then convert it into a format that will work with Express.  It will also show you how to make sure that Chrome will trust it.

This lesson is for Windows as it uses a powershell command, as well as the Microsoft Management Console (MMC)
 

Create Local SSL Certificates for an Express App on Windows

Microsoft Windows is a group of several graphical operating system families, all of which are developed, marketed, and sold by Microsoft. Each family caters to a certain sector of the computing industry.

microsoft

For a production Windows server it makes sense to run NodeJS applications as a Windows service.  This way they can be automatically restarted if the server is rebooted.

It also makes it easier to use monitoring tools like datadog to detect if there are issues with the service which is critical for production systems.

Install a Node.js Application as a Windows Service

 We map over the initial set of results then use `Promise.all` to efficiently fetch the details of each pokemon rendered in the table. This allows us to add more columns to our pokemon table.

Fetch and display row details with Promise.all in Tanstack Table

map over the initial set of results then use `Promise.all` to efficiently fetch the details of each pokemon rendered in the tanstack table

Create a custom hook to fetch remote data by passing a promise to the React 19 `use` hook. Then render this data in Tanstack Table by memoizing the results and passing them to `useReactTable`.

Render remote data in a Tanstack Table with React 19's use hook and a promise

Create a custom hook using React 19’s use to fetch data with a promise. Render it in a Tanstack Table by memoizing and using useReactTable.

You know when you're deep in the code trenches and need someone to challenge your assumptions? That's exactly what this is about.

Instead of waiting for someone to poke holes in your master plan, you can paste that strategy straight into AI Studio and let it pressure test your ideas. We're talking about using this tool to validate everything from your feature proposals to your ambitious refactoring strategies.

The real magic happens when you hit that microphone button. Now you're in a real-time verbal sparring match. It's like rubber duck debugging but the duck actually pushes back and makes you defend every architectural decision you've made.

Here's the workflow:
1. Drop your master plan into the system instructions
2. Tell the AI to challenge every assumption
3. Hit that mic and defend your choices like you mean it
4. Watch your plan either emerge stronger or get rightfully dismantled

This approach is perfect when you're:
- Stuck in your own head with a complex problem
- Need to validate if your approach holds water
- Want to catch edge cases before they catch you
- Looking for opportunities you might have missed because you're too close to the code

It's like having a senior dev on call 24/7, ready to challenge your ideas and push you to think deeper about your solutions.

Now go forth and get your ideas challenged. Your projects will thank you for it.


An Uncomfortable and Challenging Conversation with AI Studio

In this lesson, you'll learn how to effectively brainstorm your project ideas before working with AI agents. We'll cover:

1. The importance of having a clear plan before starting work with AI agents
2. Using traditional brainstorming techniques to gather your thoughts
3. Leveraging video recording to capture your brainstorming session
4. How to review and analyze websites for inspiration
5. Documenting likes, dislikes, and potential features for your project
6. Using AI tools (like AI Studio) to organize and process your recorded brainstorming session

The lesson emphasizes a modern approach to brainstorming that combines traditional techniques with video recording, making it easier to later process your ideas through AI tools. This method helps ensure that no ideas are lost and that your thoughts can be effectively organized by AI for future reference.

How to Brainstorm Your Project Features to Prepare for AI Agents

Many people share .cursorrules files, but trying to craft your own from scratch is a huge pain. Instead, this lesson shows you how to grab ALL community-created .cursorrules files to build a custom rule set for your projects. We'll use Gitingest and Google AI Studio to gather these examples and generate an awesome starting point customized to your specific project.

Generate Custom .cursorrules for Your Project Based on Community Examples

The GitHub CLI allows you to search issues and filter on different criteria like amount of comments. This allows you to search for real-world situations and pains/solutions people have using different technology. 

With the GitHub CLI in hand we'll drop 100s of issues as context in Googles AI Studio and have Gemini determine the 10 most frequently asked questions. This will give us a sense of how people use a library and where people get hung up.

Search 100s of Issues with GitHub CLI as Context for AI Studio

I often find myself wondering how to use certain APIs in certain situations, and I have a couple of keywords that I try and search up on Google. 

What you can do in this situation is turn to GitHub and see how other people have actually used this code on public projects. The GitHub CLI enables us to run searches like this, and we can take the results and work on them programmatically.

Inside of Cursor with their Composer AI agent, we can search for the files we want, tell the agent to download them, and inspect them for better understanding of how those things work together.

Search All of GitHub's Code and Pass the Results to AI Agents in Cursor

Implement virtual scrolling to handle large datasets in an HTML table. Start with a basic table that renders 10,000 rows of data. The browser can struggle with this amount of DOM nodes to render at one time, especially on low end devices. 

We implement tanstack react virtual to render only the necessary amount of rows the user is viewing. The implementation includes proper column sizing, accessibility considerations, and convenient scroll controls, resulting in a smooth and performant table experience regardless of the dataset size.

Create a row virtualized HTML table with Tanstack Virtual

Learn how to implement virtual scrolling in React tables using TanStack Table and Tanstack Virtual with the useWindowVirtualizer React hook.

The most difficult tasks will require the strongest AI models. And those models simply aren't available as AI agents inside of your tooling. So the best workflows are to drag and drop the files you need to be changed and analyzed inside of a reasoning model such as OpenAI's O1 Pro, then letting it create a giant plan for your dumber AI agents to work on. The key here is that instead of just copying and pasting the entire plan into Cursors Composer or your AI agent, it's to create a file inside of your project and ask your agent to tackle the plan one step at a time. This allows you ample opportunity between each step to test and verify the micro steps along the way. Otherwise, as I've experienced many times, a session can go way off track and you burn a lot of time and burn a lot of AI requests Cursors Composer and it starts churning through a plan Cursors Composer and making lots of unwanted changes. Cursors Composer and it's to guide an AI agent to hold its hand along the way, especially through the most complex tasks. Cursors Composer and the most complex tasks Cursors Composer and the most complex tasks require a step-by-step plan.

Planning AI Agent Sessions with Reasoning Models

AI agents will inevitably make mistakes—it's just a matter of when. A key tool in your toolbox, once they do make mistakes after working on a problem for a while, is to use **git diff** to capture all of the changes they've made. 

Once you have those changes in a text file, you have a portable document that you can test against other AI models. This allows you to move the problem into smarter reasoning models with smaller context windows, helping you isolate and focus on the issue at hand.

**git diff** has been invaluable when working with AI agents because they'll often make many good changes but also slip in one tiny error. Finding that one small mistake is **git diff**’s superpower.

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